This study will examine the health risks and benefits of moderate alcohol use in women by analyzing previously collected data from the Study of Osteoporotic Fractures (SOF). SOF is an ongoing, multi-center, prospective cohort study of risk factors for osteoporosis and fractures including 9,704 older women enrolled in 1986-1987. Specific aims include: 1) a longitudinal analysis of associations between moderate alcohol use and tests of neuromuscular and physical function previously found to be a significant and beneficial relationship on cross-sectional analysis by the principal investigator; 2) assessment of the influence of moderate alcohol use on selected incident morbidities, institutionalization, and mortality; 3) identification of subgroups of women for whom moderate alcohol use is particularly harmful for application in the development of alcohol use guidelines; and 4) description of the patterns, context, and attitudes of alcohol use among older women to determine if the beneficial effects of alcohol represent alcohol consumption itself, or if it serves as a marker for higher physical and social function. SOF data were collected using interviewer- administered questionnaires, physical examinations, and selected radiographic and laboratory tests for all subjects at one of four clinic sites on four separate occasions. Alcohol use including age of first use, age of discontinuation, and current and past frequency of use was ascertained by questionnaire at visit 1, and current status was reascertained at visit 4. Additional data on alcohol use patterns will be collected by a supplemental questionnaire from a sub-sample of 1,900 SOF participants at the Portland, Oregon site during the course of the study. Many variables such as functional status, physical performance measures, and depression scales have been collected in a serial manner, allowing assessment of changes in health status. Results would be applicable to large populations of older women providing information for appropriate alcohol use decisions. Relationships between variables will be examined using multiple linear and logistic regression models predominantly.